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As I draft my implementation, the source for that will go here | ||
A crafty implementation of Google's [MorphNet](https://arxiv.org/abs/1711.06798) (and derivative iterations) in PyTorch. |
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[metadata] | ||
description-file = README.md |
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from distutils.core import setup | ||
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with open("README.md", "r") as fh: | ||
long_description = fh.read() | ||
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setup( | ||
name='morph-py', # How you named your package folder (MyLib) | ||
packages=['morph'], # Chose the same as "name" | ||
# Start with a small number and increase it with every change you make | ||
version='0.0.2-alpha', | ||
# Chose a license from here: https://help.github.com/articles/licensing-a-repository | ||
license='GNU', | ||
# Give a short description about your library | ||
description='Automate the refinement of your neural network architecture', | ||
long_description=long_description, | ||
author='Stephen Fox', # Type in your name | ||
author_email='stevethecoder34@gmail.com', # Type in your E-Mail | ||
# Provide either the link to your github or to your website | ||
url='https://github.com/stephenjfox/Morph.py', | ||
# I explain this later on | ||
download_url='https://github.com/stephenjfox/Morph.py/archive/v0.0.1.tar.gz', | ||
keywords=[ | ||
'machine learning', 'deep learning', 'nas', 'architecture', | ||
'neural networks' | ||
], | ||
install_requires=[ # I get to this in a second | ||
'torch', | ||
'numpy', | ||
'torchvision', | ||
], | ||
classifiers=[ | ||
'Development Status :: 3 - Alpha', # either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package | ||
'Intended Audience :: Developers', # Define that your audience are developers | ||
'Intended Audience :: Science/Research', | ||
'Topic :: Software Development :: Build Tools', | ||
'License :: OSI Approved :: MIT License', # Again, pick a license | ||
'Programming Language :: Python :: 3.6', | ||
'Programming Language :: Python :: 3.7', | ||
], | ||
) |